Monitoring of soil salinity by Remote sensing & GIS in kashan area

Proceedings of The Fourth International Iran & Russia Conference 442 Monitoring of soil salinity by Remote sensing & GIS in kashan area M. Abtahi1 ,...
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Proceedings of The Fourth International Iran & Russia Conference

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Monitoring of soil salinity by Remote sensing & GIS in kashan area M. Abtahi1 , M.Pakparvar2 1-Desert research station of kashan, P.O.Box 487, Kashan,Iran ,phone: 0361-4234955,4234498,Fax:4234999, Email:[email protected] 1Fars research center for natural resources, P.O.Box 71555-617, shiraz, IRAN

Abstract A study was conducted to determine the capabilities of the successive numerical Landsat data for assessment and monitoring of soil salinization. Kashan plain, with 7220 Km2 of area, located in an arid zone of the central part of Iran, was selected as the site of investigation. It seemed to be a region prone to desertification processes. Two sorts of Landsat data: MSS (1976), TM (1998) supplementary information from soil and geo maps, surface and subsurface water data were collected.After preprocessing, the images were classified on the basis of field and subsidiary data for soil salinity. For MSS data, the Pca12, Pca34 and NDVI were merged and showed the best correlation with field samples. In TM data, merging the TM4, Pca57, Pca123 and NDVI showed the best correlation. The classification was performed by the maximum likelihood algorithm.Verification of the results showed that the differentiation between salinity classes has had a meaningful precision for both salinity maps.

A GIS network was constructed. After producing a DEM layer of the region, the other layers such as the oldest and newer maps of isodept curves of groundwater and also the salinity of groundwater maps, two classified soil salinity maps, geomap, were introduced to the GIS network as well as their documents.Merging and processing the whole data showed that 7.5% of non saline parts of region, had changed from medium to high saline, and in the same time, the size of Kashan salt lake has decreased 0.1% of total. Most of the salinized area is located in area with more salinity and reduced depth of groundwater compared with their past. Deep underlying geological material of these areas is mainly the Miocene saline evaporate deposits which is recognized as the main factor for increasing the salinity of groundwater and consequently the soil surface.

Key words: Remote sensing, Salinity, Desertification, PCA, image processing, GIS.

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Introduction Salt affected soils cover about 7 percents of the global area (szabolcs 1981). In Iran an estimated of 23.5 Mha (14.2% of total area) is affected by salinity problems (Vanaart etal 1968) and 50% of irrigated lands are salinized or prone to salinization (Pazira 1999). With this high level of salinity there is a need to monitor the problem regularly in order to take up timely reclamative and preventive measures. Remote sensing by virtue of its repetitive coverage of large areas offers great potential for monitoring dynamics, including salinization /alkalinization (Myers etal 1980, Kharin 1982, Hellden 1985, Tucker and Justice 1986, Vinogrdov 1993,). Diwivedi and Rao (1992) showed that a band combination of 1,2 and 3 bands of TM could separate degrees of salinity . A statistic index, OIF was used for band selecting. Jushi and sahai (1993), classified the salinity as severe, moderate, and slight using TM5 and MSS2 by a precision of 90 and 74 percent respectively. Damavandi (1996) showed that the best correlation between the EC of field samples and the spectral bands is obtained by TM3/4. Also a combination of CMP457 (first band from PCA on the bands 4,5,7); CMP234(first band from PCA on the bands 2,3,4) using TM4,TM3; TM6CMP1 (first band from PCA on all bands except the TM6); and TM6Cmp2 (second band from PCA on all bands except the TM6), TM4,TM2 could present the best separation of salinity classes in the regions around the Qom salt lake of Iran. Materials and methods Study site The Kashan plain is bounded by the geo coordinates 33˚,46ғ to 33˚,30ғ N and 51˚,03ғ to 52˚,28ғ E is located in the center part of Iran (200 km east of Tehran, in the Esfahan province, Figure 1). Mean annual precipitation(20 years of data) is 136.6 mm and 214 mm in the lowlands and uplands respectively. The whole basin has an area of 11000km2 .The test site is 7220 km2 in area. It is restricted by a chain of mountains to the west and south and a salt lake (Maseeleh) to north. Kashan city (350000 people) and its dependent suburban and rural parts are distributed around the plain. The plain is formed from two sorts of alluvium of Paleocene age, brought down and deposited by eleven ephemeral and perennial rivers. The alluvial fans have been gently changed to a flat evaporate salty and gypsum deposits ending in a salt lake. Because of the common wind direction from SW to NE, a chain of sand dunes, 756 km2 in area, has formed in the middle of the plain. The local lowlands that serve as accretion zones for salts and finer particles, act as evaporating pans and play a crucial role in the formation of salt affected soils.

Data base The remote sensing data base comprised Landsat2 MSS digital data of 2339 by 3264 that was acquired on 25 may 1976 and landsat5 TM of 5000 by 8944 pixels for 18 may 1998. The survey of Iran topographical sheets of 1:50000 and 250000 and published soil survey reports and geomaps as well as the data of level and quality of ground water were also used. A pentium II PC, the IDRISI 2.008, photo shop 5 were the main tools for investigation. Approach Geometric correction Eight ground control points which had been accurately obtained in the field observation by the GPS instrument, were used for geocorrection of the images. TM data obtained from Eosat, had been originally georeferenced by non-parametric systemic correction. The MSS was corrected by a linear polynomial model with the RMSE of 0.92 pixel. Its pixel size was reformatted to 25 X 25 (equal that of the originally resampled TM). Field sampling

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On the basis of the soil maps and a preliminary unsupervised classification, a network of welldistributed sampling areas were developed. Some 84 soil samples was collected from 21 training area. The samples were analyzed for texture components, caco3, gypsum, EC and SAR. Classification Original bands, a variation of spectral ratios, band combinations and Principal Component Analysis (PCA) on different bands previously proposed for separating salinity classes as well as the differentiating between gypsum and saline area were constructed (table 1). The DN values related to pixels of training samples were extracted for each of the bands or bands combinations. According to the correlation between DN values and EC, percent of gypsum and caco3 of soil samples (Table2) the best combination was selected. The Field samples were divided to two categories in a viewpoint of well spatial distribution through the area for each category. Supervised classification was done on the appropriate band combination applying the maximum likelihood algorithm on the basis of the first category.

A variation of EC classification was considered and the best result was obtained using the classification as give in Table 3. Because of the lack of observation for MSS data, a set of training pixels was selected from classified TM image as a basis for MSS supervised classification Precision analysis The second category of field samples was used for checking the precision of the classified TM data. Results showed the total precision of 92%. For the MSS, a set of 23 checking points was axtracted from the field samples (applying the local experiences and different from those had been used as the training samples).Total precision of 72% was obtained. GIS Soil surface salinity classified maps of MSS and TM were overlaid and a new map of salinity changes was constructed. This was merged to the geomap, and DEM layer and the document table of the final algorithm was constructed. Results and discussion Results of image classification are shown in figure 2 and the data relating to change of salinity through the period are presented in table 3. A cross matrix of changes between the classes is shown in table 4. According to table 4, 7.5 % of the non-saline area has been changed to saline (6.1% to class 2 and 1.4% to class 3). In the same time, 1.5 % of the area previously covered by salt lake, has changed to lower classes (0.5% to class 1, 0.1% to class 2 and 0.9% to class 3). All of the changes from non-saline to high salinity (class 1 to 3) has occurred in marginal lands.Most of the change from non saline to low and medium (class 1 to 2) and also medium to high (class 2 to 3) Is shown in central parts especially around the Kashan City and its other sub urban and villages

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Considering the whole overlaid data, it may be synthesized that salinization has accurred in relation to two main mechanisms namely exposure underlying saline layers in some marginal area because of the wind erosion which is sequentially due to over grazing and deterioration of green cover as shown in 3, and salinization of the groundwater, which is the only source of irrigation water. The latter is due to over exploitation water aquifer (more than 6 meters lowering of sub surface water and a increase in the number of wells over the 22 years period) and in the same time, a decrease in recharge from upland catchments.The increase in runoff coefficient is evidence of less water absorption by soils of uplands due to deterioration of plant cover, which in turn results in increasing sediment concentration. Two problems are caused by that phenomenon; a) In marginal area, the saline ground water front that previously had restricted and controlled by hydraulic pressure the sweet water aquifer is now allowed to flow beneath the arable lands. b) In central parts, salinized groundwater has developed on the alluvial aquifer with a very deep underlying evaporate of Miocene age.The decrease in water table (more than 20 meters in some cases) has caused more contact with saline layers and dessolution of minerals. Figure 4 is an example of degraded lands affected by newly salinized irrigation water.

Classification of the images in order to differentiation between saline and gypsum area did not give any reasonable results on the basis of the precision analysis. All of the soil samples having different quantities of gypsum over the zero had EC values above 10 ds/m. so, our problem was separation of gypsum saline area from the saline area and there was a lake of DN differentiation. However there was not a great error for detecting the salinity changes, because there was no change in gypsum area from past to present.

Conclusion The main reason for progressive salinization in the Kashan plain as a typical example of central deserts area of Iran is disturbance of hydrological cycle and mismanagement of water which reflected on soil and water salinity. In addition, plant cover degradation may be the cause of salinization of the marginal lands. PCA of blue, green and red could increase the seperability of salinity classes especially when combine with the PCA of the near IR (5 and 7)bands. Incorporation of NDVI in PCA combination, could increase the correlation of DN values and soil EC; it seems to be a way to minimize the effect of plant cover absorbency when soil salinity is the main point of notice.

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There is a need for better recognition of the interaction effects of saline gypsum and carbonate minerals in spectral reflectance as well as in selecting the best band or PCA combination for their differentiation. References 1- Alavi Panah, S. K, (1998). Study of soil salinity in desert based upon field observation, remote sensing and a GIS (case study: Ardakan area, Iran). Unpublished paper presented at the INT Symposium of New Technologies to Combat Desertification, held in Tehran, Iran, 12-15 October 1998. 2- Dapper, Goossens, (1996). Modeling and Monitoring of soil salinity and water logging hazards in the desert - delta fringes of Egypt based on geomorphology, remote sensing and GIS. Proceedings of the 16th Earsel symposium Malta, 20-23 MAY. 3- Dwivedi, R.S, Rao, BRM, (1992). The selection of the best possible landsat TM and combination for delineating salt-affected soils. INT. J. Remote sensing. vol. 13: No. 11, 2051-2058 4- Hellden, V, (1985). Remote sensing for drought impact assessment-a study of land transformation in Kordofan, Sudan. Advances-in-Space-Research, 4: 11, 165-168 5- Joshi, M.D, Sahai, B, (1993). Mapping of salt affected land in Soura Shtra coast using landsatsattelite data. INT.J. Remote sensing, vol. 14: No. 10, 1919-1929. 6- Jurio, Elsie-M, Zuidam,(1998). Remote sensing, synergism and information system for desertification analysis: an example from northwest geographical patagonia. Argentina. ITC Journal 3/4. 7- Kharin, N. G, (1982). Remote sensing and monitoring of desertification in arid lands. Alternative strategies for desert development and management, vol. 4 (UN Institute for Training and Research) 1295-1309, New York, USA, Pergamon press. 8- Kaushalya, Ramachandran, (1992). Monitoring the impact of desertification in Western Rajasthan using remote sensing. Journal of Arid Environments 22: 293-304. 9- Lyon, Johng, Yuan, Ding, ET al, (1998). A change detection experiment using vegetation 10- indices. PE and RS. February. 11- Mishra, J.K, Joshi, M.D. (1994). Study of desertification Process in Aravialli using remote sensing techniques. INT.J. Remote sensing, vol. 1: No. 1, 87-94. 12- Metternicht, Graciela, Zinck, Alfred, (1996). Modeling salinity alkalinity classes for mapping salt-affected top soils in the semi arid valleys of Cochabamba (Bolivia). ITC Journal, vol. 2, 125-134 13- Myers, V. I, Mann, H.S, Moore, D, Derries, M, Abdel-hady, M, (1980). Remote sensing for monitoring resources for development and conservation of desert and semi-desert areas. Mann, H.S (Ed): Arid zone research and development, 505-513 14- Tucker, C. J, Justice, C. O, (1986). Satellite remote sensing of desert spatial extent. Desertification Control Bulletin, no. 13: 2-5 15- Rao, BRM, Dwivedi, RS, (1998). An inventory of salt-affected soils and water logged areas in thenagar Jun Sagar canal command area of southern India, using multispectral space-Borne data. Land degrad. develop. 9: 357-367. 16- Vinogradov, B. V, (1993). Remote indicators of soil desertification and degradation. Eurasian-Soil-Science, 25: 8, 66-75

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Fig.1. Location of the Kashan plain

Table 1 – List of combinations which have compared for salinity classification Type of band or combination

Description

Objective

Source

TM 1 to 7 except 3

Combination

Salinity alkalinity

Metternicht and Zinck (1996)

TM 1,3,5

Combination

Salinity classes

Dwivedi and Rao (1992)

TM 3,4,5,6

Combination

Salinity and gypsum

Alvi Panah (1997)

TM 2,3,4

Combination

Salinity alkalinity

Rao and Dwivedi (1998)

TM 3 / 4

Spectral ratio

Correlation against EC

Damavandi (1996)

TM 4 / 3

Spectral ratio

Minimizing the salinity interfere Lyon and Yuan (1998)

TM 1,2,4,5

Combination

Salinity classes

Joshi and Sahai (1993)

TM 5

Original band

Salinity mapping

Joshi and Sahai (1993)

CMP457; CMP234,TM4,TM3; and TM6CMP1;TM6CMP2,TM4,TM2

Combination of PCA and Salinity classes original bands

TM4, PCA57, NDVI

PCA123

and Combination of PCA and Salinity classes original bands

Damavandi (1996) Proposed in this work

MSS 2

Original band

Salinity mapping

Joshi and Sahai (1993)

PCA of MSS

Higher order of PCA

Salinity classes

Dwivedi (1996)

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PCA12, PCA34 and NDVI

Combination

448

Salinity classes

Proposed in this work

Table 2 – correlation between DN values and soil properties

Soil properties

Type of band or combination

EC ds/m

Gypsum%

%Carbanate

Correlation

Significant level

Correlation (a)

Significant level

Correlation (b)

Significant level

TM 1 to 7 except 3

0.57

0.001

0.36

0.05

0.25

NS

TM 1,3,5

0.21

0.05

0.11

NS

0.22

NS

TM 3,4,5,6

0.31

0.01

0.42

0.01

0.26

NS

TM 2,3,4

0.48

0.001

0.22

NS

0.24

NS

TM 3 / 4

0.51

0.001

0.28

NS

0.35

NS

TM 4 / 3

0.28

0.01

0.25

NS

0.28

0.05

TM 1,2,4,5

0.51

0.001

0.15

NS

0.24

NS

TM 5

0.41

0.001

0.20

NS

0.21

NS

0.65

0.001

0.21

NS

0.35

0.05

TM4, PCA57, PCA123 and NDVI

0.79

0.001

0.22

NS

0.33

0.05

MSS 2

0.39

0.001

0.12

NS

0.26

NS

MSS 3

0.44

0.001

0.13

NS

0.29

NS

PCA12, PCA34 and NDVI

0.66

0.001

0.21

NS

0.ż

0.05

CMP457; CMP234,TM4,TM3; TM6CMP1; and TM6CMP2,TM4,TM2

a and b – some samples having zero values for the property were omitted Table 3 – Salinity classes based upon the field samples and the image characteristics

No. of class

Description

Range of EC ds/m

1

Non saline

0–2

2

Low to medium salinity

2 – 10

3

High salinity

> 10

4

Salt flat

-

Table 4. Change in salinity classes through the 22 years period Class

Description

EC ds/m

Size of area (1976)

Size of area (1998)

Ha

%

Ha

%

Deference

Change at the class %

Total change %

1

Non saline

0–2

209133

29

154781

21.4

-54352

-26

-7.5

2

Low to med.

2 – 10

301650

41.8

310437

43

8787

2.9

1.2

3

High

> 10

112917

15.6

169577.8

23.5

56660.8

50.2

7.8

4

Lake

-

98510

13.6

87414.2

12.1

-11095.8

-11.3

-1.5

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722210

100

722210

449 100

0

-

Figure 2 – Kashan plain classified images of MSS (1976) right and TM (1998) left. Table 5 . Cross matrix of changes between the classes 1976

Non saline

Low to medium

High

Salt lake

Percent of change

Non saline

-

-6.1

-1.9

0.5

-7.5

Low to medium

6.1

-

-5

0.1

1.2

High

1.9

5

-

0.9

7.8

Salt lake

-0.5

-0.1

-0.9

-

-1.5

1998

0

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Fig.3. Marginal lands with apearance of saline layers

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Fig.4. An example of degraded lands Underlying which were arable farmlands in the past

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Regional variability and availability of cadmium in relation to soil parameters and land use type M. Amini1, M. Afyuni1, H. Khademi1, K. C. Abbaspour2, and R. Schulin3 1- Dep. of Soil Sci., College of Agriculture, Isfahan University of Technology, Isfahan, Iran. Email: [email protected], [email protected] , [email protected] 2- Swiss Federal Institute for Environmental Science and Technology (EAWAG), Ueberlandstrasse 133, P.O. Box 611, CH-8600 Dübendorf, Switzerland. Email: [email protected] 3- Institute of Terrestrial Ecology, ETH Zurich, Grabenstr. 11a, CH-8952 Schlieren, Switzerland.

Abstract Due to potential toxicity problems, the concentration of cadmium in soils is of great environmental concern. To evaluate spatial distributionof Cd in the topsoil of agricultural, industrial and urban land around Isfahan, central Iran, we collected 255 topsoil samples (0-20 cm) from the nodes of a randomized grid in a study area of 6800 km2 and measured total and DTPA-extractable Cd concentrations, soil pH, organic matter (OM), clay content, soil salinity, and chloride concentrations. The total Cd concentration exceeded the suggested Swiss thresholds of 0.8 mg kg-1 in 90% of the samples. Land use had a significant effect on total concentration of Cd in the soil but had no effect on DTPA-extractable Cd. High values of total Cd were found in industrial and urban areas, whereas low values occurred in uncultivated lands. Correlation analysis revealed that soil salinity alone explained 36% of the Cd variation in the entire study area. The correlation was particularly strong in uncultivated areas (R2 = 0.70).. Keywords: Cadmium, salinity, spatial variation, soil pollution

Introduction Environmental contamination by cadmium is a serious and growing concern. Globally, a huge amount of this metal enters the soil by human activities (Tiller et al., 1999). If Cd continues to accumulate in soils, particularly in agricultural lands, it will eventually decrease soil quality and threaten food safety and security. The uptake of cadmium by plants and ingestion by humans depends on its bioavailability in the soil (Yong, 2001). The bioavailability of Cd in soil is controlled by the total concentration of Cd as well as soil factors such as pH (Chlopecka et al., 1996), OM and clay content (McBride et al., 1997; Kabata-Pendias and Pendias, 2001), soil salinity (McLaughlin et al., 1994), concentration of chloride (Weggler et al., 2004) and carbonate (Renella et al., 2004). Therefore, accounting for both, total and available concentrations of heavy metals is recommended for hazard assessment (Wang, 1999). The spatial distribution of Cd has been studied using geotatistics (Atteia et al., 1994; Cattle et al., 2002; Van Meirvenne and Goovaerts, 2001; Meuli et al., 1998). There are only few studies that also accounted for soil properties in the spatial evaluation of Cd contamination (Van Meirvenne and Goovaerts, 2001) particularly for arid region. This study was conducted in the main agricultural, industrial and urban area of the province of Isfahan, central Iran. The objectives were to 1) assess the total and available concentration of Cd in the soils in the regional scale, 2) evaluate the effects of different land uses on the concentration of Cd, and 3) evaluate the effect of soil properties on the availability of Cd in a large scale. Materials and Methods

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The study area is about 6800 km2 around the Zayandehrood River which flows from west to southeast in central Iran (Fig. 1). The area extends from easting of 51 ,15′ to 52  , 41′, 42′′ longitude and northing of 32  ,31′,30′′ to 32  , 59′, 48′′ latitude. This area covers different land uses including agricultural, industrial, urban, and uncultivated lands. Agricultural, industrial, and urban activities are concentrated around the river and mainly in central and western part of the region. The eastern part is completely rural. A total of 255 topsoil samples (0-20 cm) were collected in the region using a stratified random sampling technique. The sampling distances varied from 3 to 5 km. The sampling pattern is shown in Fig. 1. Each point in this diagram represents a 10×10 m block in which five samples were collected from the corners and the center point and mixed to give one composite sample. Soil samples were air-dried and passed through a 2-mm sieve. Total Cd was extracted using concentrated HCl and HNO3 (Cao et al., 1984). Plant available Cd was extracted using a 0.05M DTPA extracting solution (Lindsay and Norvell, 1978). The concentration of Cd in the extracts was analyzed by atomic absorption spectrophotometry. Electrical conductivity and pH of the soil samples were measured in a 1:2.5 soil to water ratio suspension. Clay content was determined using a hydrometer. Organic matter was determined by wet oxidation (Walkley and Black). Results and Discussions The summary statistics of total and DTPA-extractable concentrations of Cd are given in Table 1. The total concentration of Cd shows a nearly normal distribution (Fig. 2), whereas the distribution of DTPA extractable concentrations is strongly skeweed. Based on the coefficient of variation (CV), the DTPA extractable Cd was nearly twice as variable as the total concentration of Cd over the regional scale. The regional mean of total Cd concentration in the study area exceeded the guide value of 0.8 mg kg-1 set by the Swiss VBBo (FOEFL, 1998), and also the maximum allowable limit (1 mg kg-1) set by the United Kingdom (Kabata-Pendias and Pendias, 2001). But it is less than the threshold recommended for calcareous soils (2 mg kg-1) by Wang (1999). The DTPAextractable Cd was less than 0.1 mg kg-1 in about 80% of soil samples. The statistical summary of clay content, organic matter, pH, soil salinity and concentration of chloride are also included in Table 1. The average soil organic matter content in the region was very low (10 dS/m). In salt affected soils, the formation of Cd and Cl complexes (KabataPendias and Pendias, 2001; Weggler et al., 2004) and competition of Cd with Ca and Mg (Yong, 2001) could result in increasing the Cd availability. Based on the results of the correlation analysis we only used soil salinity as auxiliary variable in the future spatial assessment of Cd contamination. 3.3 Spatial assessment of Cd and soil salinity Experimental variograms for total Cd, log of DTPA extractable Cd, and log of EC were computed and a theoretical model was fitted to each of them by using the least square criterion. The model parameters are summarized in Table 3. The mean estimation error (ME) and mean square estimation error (MSE) of predicted compared to measured values were used to evaluate the variogram models by means of cross validation. We found that the spherical structure of variogram gave the best result for total and DTPA extractable concentration of Cd. The computed variograms showed continuous variation in distances less than 30 km for all variables. Total and DTPA extractable concentrations of Cd were interpolated using ordinary kriging. As we expected, maximum values of total concentrations were found around a mine in the central part of the study area and also around the industrial area of Isfahan (> 2.5 mg kg-1) (Fig. 4). In most parts of the study area along the Zaindehroud River, which include the main agricultural and urban areas, the total concentrations of Cd were above 1.5 mg kg-1. In the

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uncultivated or bare soils extending from the Northwest to the Northeast total concentration of Cd did not exceed 1.5 mg kg-1. The map of available cadmium is shown in Fig 5. There are two hot spot areas. The first one is located around the mine in the west of the region. In this area the availability of Cd seems to be controlled by the total concentration of Cd (Fig. 5) Since, agricultural activities are mostly concentrated in the western part of the region, then high Cd concentrations raise concerns about food safety as Cd readily taken up by plants Moreover, most of the industrial factories, particularly metallurgical activities, are located in this part of the region. Therefore, atmospheric deposition of Cd containing dusts may aggravate this problem. Another hot spot of available Cd extends from the South to the North-west of the study region here the total concentration of Cd was relatively small. The shape and position of this area correspond to that of highly saline soils in the region (Fig 6) then soils are mostly not used for crop production. Hence, the risk of Cd entering the food chain is small. Because this area is severely affected by wind erosion, however, the Cd enriched soil particles could easily be eroded and transferred to the agricultural and densely populated urban areas where they could pose a threat to human health. Our results indicate that quantification of total Cd concentration alone is not sufficient for an accurate assessment of the risks associated with soil contamination. Other soil factors affecting the bioavailability of this metal should also be considered. For our study area, quantification of salinity and chloride concentrations is recommended in addition to Cd concentrations to assess soil pollution by Cd. References Atteia, O., Dubois, J. P., and Webster, R. (1994). Geostatistical analysis of soil contamination in the Swiss Jura. Environ. Pollu. 86, 315-327. Cao, H. F., Chang, A. C., and Page, A. L. (1984). Heavy metal contents of sludge-treated soils as determined by three extraction procedures. J. Environ. Qual. 13, 632-634. Cattle, A. J., McBratney, A. B., and Minasny, B. (2002). Kriging methodes evaluation for assessing the spatial distribution of Urban soil lead contamination. J. Environ. Qual. 319, 1576-1588. Chelopecka, A., Bacon, J. R., Wilson, M. J. and Kay, J. (1996). Forms of cadmium, lead, and zinc in contaminated soils from southwest Poland. J. Environ. Qual. 25, 69-79. FOEFL (Swiss Federal Office of Environment, Forest and Landscape). (1998). Commentary on the ordinance relating to pollutants in soils (VBBo of July 1, 1998), Bern. Kabata-Pendias and Pendias, A., and Pendias, H. (2001). Trace elements in soils and plants. 3rd Ed., CRC Press, USA, 413P. Khoshgoftarmanesh, A. H., Shariatmadari, H., and Parker, D. (2002). Effect of salinity on phytoavailability of cadmium and zinc. 17 Inter. Soil Congress, World congress of soil sci. Bankok, Thailand, 4-22 Aug. (2002). Lindsay, W. L., and Norvell, W. A. (1978). Development of a DTPA test for zinc, iron, manganese and copper. Soil Sci. Soc. Am. J., 42, 421-428. McBride, M. B., Sauve, S., and Hendershot, W. (1997). Solubility control of Cu, Zn, Cd and Pb in contaminated soils. Eur. J. Soil Sci. 48, 337-346. McLaughin, M. L., Palmer, L. T., Tiller, K. G., Beech, T. A., and Smart, M. K. (1994). Increased soil salinity causes elevated cadmium concentration in field-grown potato tubers. J. Environ. Qual. 23, 1013-1018. Meuli, R., Schulin, R. and Webster, R. (1998). Experience with the replication of regional survey of soil pollution. Environ. Pollu. 101, 311-320.

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Renella, G., Adamo, P., Bianco, M: R., Landi, L., Violante, P., and Nannipieri, P. (2004). Availability and speciation of cadmium added to a calcareous soil under various managements. European J. of Soil Sci. 55, 123-133. Tiller, K. G., McLaughlin, M. J., and Roberts, A. H. C. (1999). Environmental impacts of heavy metal in Agroecosystems and amelioration strategies in Oceania. In Huang, P. M., and Iskander, I. K., Soils and goundwater pollution and remediation, Lewis, USA. Van Meirvenne, M., and Goovaerts, P. (2001). Evaluating the probability of exceeding a site specific soil cadmium contamination threshold. Geoderma, 102, 63-88. Wang, H. K. (1999). Heavy metal pollution in soils and its remedial measures and restoration in Mainland China, In Huang, P. M., and Iskander, I. K., Soils and goundwater pollution and remediation, Lewis, USA. Weggler, K., McLaughlin, M. J., and Graham, R. D. (2004). Effect of chloride in soil solution and the plant availability of biosolid-borne cadmium. J. Environ. Qual. 33, 496504. Table 1. Statistical summary of total and DTPA extractable Cd and other measured soil variables † The unit % of Data s Total Cd † are † DTPA-Cd mg Clay (%) kgOM (%) 1 . EC (dS/m)†† †† Cl (mg/l) EC pH†† and pH were determined in a 1:2.5 suspension (soil /water). In each row the different letters show that the difference between the land uses is significant. Table 2. Correlation coefficient between the measured variables. Total Cd (mg/kg) DTPA-Cd (mg/kg) Agr. Urb. Uncul. Total Agr. Urb. Uncul. Total Total Cd 1 1 1 1 DTPA-Cd 0.14 0.24 -0.16 -0.01 1 1 1 1 ** * -0.18 -0.08 -0.1 -0.19 0.07 0.40 0.07 Clay( %) -0.29 0.11 OM (%) -0.12 -0.10 0.11 0.06 0.16 0.58** 0.17* 0.22* 0.25 0.84** 0.60** EC(dS/m) -0.16 -0.22 -0.36** -0.28** ** Cl (mg/l) -0.04 -0.47 -0.15 -0.15 0.27 0.55 0.82 0.59** ** * pH 0.27 0.13 0.08 0.11 -0.16 -0.44 -0.05 -0.07 ** and * means correlation is significant at 0.01 and 0.05 levels, respectively. Agr, Urb, and Uncul means agriculture, urban and uncultivated area respectively. Agricultural Mean CV 47 1.79a 0.26 a 0.08 0.63 29.86 a 0.37 1.22 a 0.61 3.15 a 1.15 5.63 a 2.82 7.8 a 0.03

Urban Mean CV 10 2.07b 0.39 a 0.08 0.38 21.00 b 0.50 1.04 a 0.93 2.17 a 1.19 a 4.07 1.90 7.94 b 0.04

Uncultivated Mean CV 43 1.61a 0.37 a 0.09 0.67 19.61 b 0.51 0.43 b 0.95 11.70b 1.45 17.43 b 3.53 8.7 b 0.03

Total Mean CV 100 1.74 0.33 0.09 0.56 24.48 0.47 0.85 0.87 6.89 1.80 10.77 3.98 7.89 0.03

Table 3 The parameters of fitted variograms and cross validation criteria. Variable Model C0 C-C0 a ME MSE Total Cd Spherical 0.16 0.13 30 0.012 0.14 † Log(DTPA-Cd) Spherical 0.68 0.35 31.8 -0.004 0.58 Spherical 70 80 32 -0.03 58.65 Log(EC)† C0, C and a are the nugget, sill, and range of variograms, respectively.

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The data was back-transferred before calculating ME and MSE

+ l 

 Isfahan city  Mining application

Sampling i Industrial sites

Zayandehroud 0

30

60

km

Figure 1 The study region and sampling scheme.

70 60

80

Frequency

Frequency

50 40 30

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20 10 0

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20 0

.14

.70 1.26 1.81 2.37 2.93 3.49 4.04 4.60 5.16

.01

.06

.11

.16

.21

.26

.31

.36

.41

.46

Concentration Concentration ( /k ) of total (a) and DTPA extractable (b) concentration of Cd. Figure2 Distribution 0.35

DTPA- Cd (mg/kg)

0.3 0.25 0.2 0.15 0.1 0.05 0 0

20

40

60

80

EC (dS/m)

Figure 3 Scatter plot of available cadmium versus soil salinity in uncultivated soils.

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Figure 4 Map of total Cd concentration (mgkg-1).

Figure 5 Map of DTPA- extractable Cd (mgkg-1).

EC (dSm-1)

Kilometer

Figure 6 Map of soil electrical conductivity (dSm-1).

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Nitrogen and Phosphorus Fertilizers Affect Flavonoids Contents of St. John’s Wort ( Hypericum perforatum L.) Majid Azizi1, Alberto Dias2 1-Department of Horticulture , College of Agriculture , Ferdowsi University , Mashhad , IRAN. Phone: +98511-8795618 E-Mail: [email protected] or [email protected] 2-Department of Biology, Minho University, PORTUGAL, PHONE: -351-53604317/8 E-MAIL: [email protected]

Abstract Saint John′s wort ( Hypericum perforatum L. ) is a valuable medicinal plant that has been used since ancient time due to producing a wide range of secondary metobolites with significant pharmaceutical effects such as wound healing and antidepressant properties. There are 60 herbal drugs that originate from the plants. In Iran there are 3 herbal drugs that contained St. John’s wort. It contains several important secondary metabolites such as naphthodianthrones (Hypericin and Pseudohypericin), phloroglucionols (Hyperforin and Adhyperforin), flavonoids (chlorogenic and isochlorogenic acid, apigenin, biapigenin, rutin, quercetin, isoquercetin, amentoflavon) and essential oils. In this research we conducted the field trial in two successive years for studying the effects of three levels of nitrogen (zero, 75, 125 KgN/ha) and three levels of phosphorus fertilizers (zero, 50 and 100 KgP2O5/ha) on flavonoids content of the plants by HPLC-DAD method. Statistical design was RCBD with three replicates. We analyzed chlorogenic and isochlorogenic acid , apigenin , biapigenin , rutin , quercetin , isoquercetin and amentoflavon of the samples. Our results show that nutrition of Hypericum perforatum with the fertilizers can improved drug quality by changing of flowering habits and flavonoids content of the plant in comparison with control treatment. Optimum fertilizer treatment for production of high dry herb yield and flavonoids content was 125 Kg/ha nitrogen and 50 Kg P2O5/ha phosphorus fertilizer. Key Words index : Fertilizer, Flavonoids,HPLC-DAD, St.John’s wort

Introduction St. John’s wort (Hypericum perforatum L.) is considered an important source of pharmaceutical and dietary supplements. It occurs naturally in the Northern of Iran, Asia Minor, Europe and Northern Africa (Bombardelli and Morazzoni,1995; Cellarova et al 1995). It has pharmaceutical effects such as wound healing and antidepressant properties(Chatterjee et al 1998;ESCOPE,1996). The most important secondary metabolites in Hypericum perforatum are naphthodianthrones (hypericin and pseudohypericin), acylphloroglucinols (hyperforin and adhyperforin), essential oils and flavonoids (American Herbal Pharmacopoeia, 1997; Anoun, 1991; Erdelmeier et al, 1998; Maisenbacher and Kovar,1992). Culture extension of medicinal plants need to optimizing cultural practices such as nutrition, irrigation, harvest time and etc (Azizi,2001;Mathe,1988). The formation of typical secondary metabolites as well as physiological and biochemical processes depend on the actual environmental conditions. Other than genetic and ontogenetic factors, the environmental factors take only quantitatively modifying effect. The edaphic factors, among the environmental factors, are of specific importance with regards to plant production (Franz,ch,1983). Bernath(1986) also illustrated that relationship between the nutrient elements and metabolic processes leading to the formation of special products in medicinal plants is certainly important. In this research we studied the effect of nitrogen and phosphorus fertilizers on the flavonoids content of Hypericum perforatum L.

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MATERIAL AND METHODS F i e l d e x p e r i m e n t a n d b i o l o g i c a l m a t e r i a l . The research was conducted in the Agricultural Research Station of Tarbiat Modares University (North of Tehran) during 2000-2002. Seeds of “Topas” cultivar of St. John’s wort were washed overnight in tap water and air-dried. They were sown in outdoor bed and irrigated regularly. Seedlings were transplanted to the field when they were 25 centimeter length . The statistical design used in the research was randomized complete block design(RCBD) with nine treatments and four replicates. Fertilizers treatments included three levels of nitrogen (zero, 75, 125 Kg N/ha) and three levels of phosphorus fertilizers (zero, 50 and 100 Kg P2O5/ha). Each plot was 160×125 centimeter and seedlings were planted in 40×25 centimeter distances. In the next year the flowering plants were harvested (top 10 centimeter) and dried in dark condition at 30 ± 5 degree centigrade for 3-5 days. HPLC-DAD analysis. Extracts were prepared from dried biomass (0.2-0.3 g) by sonication at room temperature with 10 ml of a methanol-water solution (80:20). Solutions were filtered through a 0.2-µm filter and were analyzed by HPLC-DAD as described elsewhere (Dias et al. 1998, 1999). The secondary metabolites quantification were done by the external standard method using a standard solution. Identified compound with their respective retention time and selected elution gradient time shown in Table 1 and 2 respectively.

Results and Discussion Our results presented in Table 2 and 3. As shown in Table 2, nitrogen and phosphorus treatments had a significant effect (Į=0.01) on number of stem/plants in successive years. The highest number of stem/plant in the both year (16.55 and 18.13 respectively) belong to N125P100 treatment and the lowest one (8.89 and 10.35 respectively) belong to check treatment (Table2). Nitrogen and phosphorus treatments also had a significant effect on dry herb yield. Our results shown that the lowest dry herb yield produced in control plot in both years (745.883 and 683.46 g/m2 respectively). The highest dry herb yield in the first year (984.567 g/m2) produced in plots received medium and high level of nitrogen. The lowest dry herb yield produced in check treatments in both year (745.883 and 683.46 g/m2 respectively)(Table2). Fertilizer treatments also affect biochemical properties of Hypericum perforatum. Chlorogenic acid content significantly affected by nitrogen fertilizers(Table 3). The highest content of chlorogenic acid produced in N0P100 and the lowest one belong to N125P100. Nitrogen application decreased the chlorogenic acid content. The higher the nitrogen concentration the lower the chlorogenic acid content. Analysis of variance of data showed that NP supply had not significant effect on isochlorogenic acid content. Such as the chlorogenic acid, the highest isochlorogenic acid content detected in N0P100 treatment and the lowest one in control plots(4899.644 and 3708.073611µg/gD.W. respectively). Quercetin analysis of the samples did not show significant differences between the treatments (Table3). Our data about the rutin content suggest that the lowest rutin content (10556.56611µg/gD.W.) produced in N0P50 and the highest content(52809.13611µg/gD.W.) in N75P50. Biapigenin analysis of treated Hypericum perforatum shown significant effect of NP supply, as phosphorus fertilizers decreased biapigenin but nitrogen fertilizers increased it. ANOVA of the results shown an interaction between nitrogen and phosphorus fertilizers in biapigenin production. The highest biapigenin content (408.611µg/gD.W.) belong to N125P0 and the lowest content (162.383 µg/gD.W.) belong to N75P0. There is not significant differences between amentoflavon content of samples (exception N125P50 that shown higher amentoflavon compare to control(Table3). Sulisbury and Ross(1992) presented that nitrogen fertilizers increased plant sensitivity to pathogen by increasing chlorogenic acid content . Davis and et al (1988) shown that chlorogenic acid has an important role in adventitious root

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formation in cutting. Omidbaigi and Azizi(2000) shown that other than environmental factors , harvest time has an important effects on biochemical properties of the plants. They shown that full flowering time is the best harvest time for St.John’s wort production. In conclusion , phytochemical properties of medicinal plants affected by several factors such as cultural practices, environmental condition, genetic properties and developmental stage. Martonfi and Repcak(1994) and Repcak and Martonfi(1997) shown that flowers parts are the main organs that produced active principles in Hypericum perforatum therefore each treatments that changed the ratio of flower/herb affected active substances properties of St.John’s wort. Chemical and physical properties of soil affect directly or indirectly active substances content of medicinal plants(Bernath,1986;Franz,1983). In this research NP supply affect flavonoids content of St.John’s wort indirectly by changing of the flower/herb ratio. Our results comparision with other research (Umek et al, 1999) shown that active substances of cultivated Hypericum perforatum in Iran are in a good situation suitable for production herbal drugs. References American Herbal Pharmacopoeia and Therapeutic Compendium(1997) St.John’s Wort , Hypericum perforatum .32pp. Anoun , (1991) St. John′s Wort ( H . perforatum ) , Hungarian standards (No 19884 ) . Azizi,M. (2001) Effects of some environmental and physiological factors (in vivo and in vitro) on growth , yield and active substances of Hypericum parforatum L.). Ph.D dissertation, Tarbiat Modarres University ,Tehran ,Iran. Bernath,J. (1986) Production ecology of secondary plants products . In : Herb , Spice and medicinal plant , Volume 1 Oryx Press.Arzona.185-234. Bombardelli, E. and P. Morazzoni, (1995) Hypericum perforatum. Fitoterapia , 66: 1, 4368. 6-Cellarova , E., K. Kimakova , Z. Daxnerova , and P. Martonfi , (1995) Hypericum perforatum ( St Johns wort ) : In vitro culture and the production of Hypericin and other Secondary Metabolits. In : Bajaj , .P.S.(ed): Biotechnology in Agriculture and Forestry , vol 33 : Medicinal and Aromatic plant VIII.Berlin,Heidelberg , Springer-verlag , 1995 , 261275 Chatterjee,S., M.Noldner, E. Koch, C. Erdelmeier, and WE. Muller, (1998) Antideppressant activity of Hypericum perforatum and hyperforin , The neglected possibility .Pharmacopsychiatry, 30:22-28 . Davis ,T.D., B. Haissig, and N.Sankhla. (1988). Adventitious Root Formation in Cuttings. Dioscorides press ,315pp. Dias , A.C.P., R.M. Seabra, P.B. Andrade, F. Ferreres, and M. Fernandes-ferreira, (1998) Differential phenolic production in in vivo plants and in vitro culture of Hypericum perforatum L. and Hypericum androsaemum L. 46th annual congress of the society for medicinal plant resaerch, Vienna, Austria, C24 . Dias , A.C.P., R.M. Seabra, P.B. Andrade, F. Ferreres, and M. Fernandes-ferreira, (1999) The development and evaluation of an HPLC-DAD method for the analysis of the phenolic fractions from In Vivo and In vitro biomass of Hypericum species . J. LIQ. CHROM. & REL. TECHNOL. ,22 (2): 215-227. Erdelmeier, CAJ., W. Muller, and SS. Chatterjee, ( 1998) Hyperforin possibly the major nonnitrogenous secondary metabolites of Hypericum perforatum l. Pharmacopsychiatry,31:1-6. ESCOP. (1996) Monographs on the medicinal use of plants drugs , fasicule 1 , Hyperici herba. Franz,Ch. (1983) . Nutrient and water management for medicinal and aromatic plant. Acta Hort.132:203-215.

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Maisenbacher,P. and K.A. Kovar, (1992) Adhyperforin: A homologue of hyperforin from Hypericum perforatum L. Planta Medica 58:291-293. Martonfi, P. and M. Repcak, (1994) Secondary metabolites during flower ontogenesis of Hypericum perforatum L. Zahradnictvi , 21: 1, 37-44. Mathe, A. (1988) An Ecological Approach to Medicinal Plant Introduction . In : Herb,Spice and Medicinal Plants, Recent Advances in Botany, Horticulture and Pharmacology, Volume 3:175-205. Omidbaigi ,R. and M. Azizi . 2000. Effect of time of harvest on hypericin and essential oil content of Hypericum perforatum L. Iran Agricultural Research.19:155-164. Repcak, M. and P. Martonfi, (1997) The localization of secondary substances in Hypericum perforatum flower. Biologia-Bratislava , 52: 1 , 91-94 . Salisbury, F.B. and C.W. Ross. 1992 . Plant Physiology, Wadsworth Publishing Company. 682pp. Umek, A., S. Kreft, Th. Kartnig, and B. Heydel, (1999) Quantitative phytochemical analysis of six Hypericum species growing in Slovenia. Planta Medica(65): 388-390 .

Table 1- Compound identified in the methanolic extracrts of Hypericum perforatum L. biomass with their respective retention time Compound Isochlorogenic acid Chlorogenic acid Biapigenine Rutin Quercetin amentoflavon

Retention time(minute) 3.13 4.19 63.88 11.74 20.85 31.21

Table2-Effects of nitrogen and phosphorus fertilizers on yield parameters in Hypericum perforatum Treatments No of Flowering stem/plant Dry herb yield (g/m2) First year Second year First year Second year N0P0 8.89 d 10.53 e 745.883 c 683.46 c N0P50 13.44 bc 11.23 e 829.817 bc 832.92 bc N0P100 11.64 c 14.13 d 789.25 c 751.26 bc N75P0 13.8 bc 16.05 bc 984.567 a 872.82 bc N75P50 14.25 ab 17.5 ab 832.133 bc 1105.45 ab N75P100 15.46 ab 14.76 cd 961.483 a 865.44 bc N125P0 13.19 bc 15.1 cd 932.883 a 1020.06 ab N125P50 15.13 ab 17.43 ab 894.133 ab 1155.34 a N125P100 16.55 a 18.13 a 964.85 a 917.86 abc Means in each column compared using Duncan’s multiple range test (0.05)

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Table3-Effects of nitrogen and phosphorus fertilizers on flavonoids content of Hypericum perforatum Treatments Flavonoids contents(Microgram/g.dry weight basis) Chlorogenic acid N0P0 1516.516ab N0P50 1528.743ab N0P100 1583.742a N75P0 1203.287bc N75P50 1401.248abc N75P100 1166.602c N125P0 1361.381abc N125P50 1247.542bc N125P100 1156.402c

Isochlorogenic acid

3708.073b 4164.929ab 4899.644a 4054.709b 3979.56b 4173.752ab 4445.472ab 3983.881b 3808.619b

Rutin 11239.81b 10556.56b 12259.67b 9716.715b 52809.13a 10819.8b 11029.46b 12154.84b 11186.56b

Quercetin 2259.037a 2266.935a 1969.244a 1552.837a 1255.928a 1570.459a 1345.891a 2168.608a 2066.704a

Amentoflavon

Biapigenine

25.9970b 24.0636b 13.974b 18.6700b 12.4287b 11.8520b 15.9895b 29.6191a 14.7941b

372.907ab 294.381abc 167.616c 162.383c 203.015bc 188.500bc 408.611a 302.151abc 215.712bc

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Lead contamination and impacts on microorganisms in the vicinal soils of Razan-Hamadan highway, Iran Parveneh Ebrahimi1 and Ali Akbar Safari Sinegani2 1-Soil Science Department, Agriculture Faculty, Bu-Ali Sina University, Hamadan, Iran. Phone: +98-811-4223367 Email: [email protected]; 2- Soil Science Department, Agriculture Faculty, Bu-Ali Sina University, Hamadan, Iran. Phone: +98-811-4223367. Email: [email protected].

Abstract Soil pollution is one of the most important ecological problems in the present time. Ecological consequences of this are numerous: disturbances in soil functionality, effect on the incidence of soil micro-organisms, and changes in activities and community composition of the soil microorganisms. On the other hand, soil micro-organisms and their activities may be sensitive indicators of soil pollution due to their ubiquity, direct contact with soil particles and relatively short generation time. The contribution to environmental pollution of heavy metals from automotive emissions has been the subject of intensive investigation in recent years. Airborne metal particulates, such as Pb, have been attributed mainly to emissions from motor vehicle exhausts. The objective of this study was to determine the lead contamination of soil and its effect on some soil microbiological characteristics. In both sides of Razan-Hamadan highway sampling was carried out on a transect (200-m long), in vertical direction from highway with a separation distance of 10 m, thereby providing 20 actual sampling locations for each side of highway. Soil samples were taken from 0-15cm depth with 3 replicates. Soil samples were analyzed for total Pb, VAM spore numbers, substrate- induced respiration (SIR), total plate numbers of bacteria and actinomycetes. Total Pb content of soil samples decreased exponentially with increase of distance from the roadsides. Strikingly, VAM spores numbers were high in soil samples taken from polluted sites. They decreased exponentially with increasing distance from the roadsides, same as soil total Pb. Substrate-induced respiration, bacteria, and actinomycetes numbers were relatively high in soil samples taken from relatively unpolluted sites. They initially increased with increasing distance from the roadsides and then decreased. Correlation studies showed that there is a positive relation between the studied biological indices and soil total Pb. This result may be related to higher root density (organic carbon) and soil fertility near roadsides alleviating the negative effects of soil Pb contamination. Key Words: Actinomycetes, Bacteria, Lead pollution, SIR, VAM.

Introduction In recent decades, there has been an increasing interest in heavy metals, not only because of toxicity to animals, plant, and other living organism, but also because they can not be removed from the soils, as they become irreversibly immobilized within different soil components such as Fe and Al oxides and hydroxides, clay particles, etc. There is concern about the long-term effect of these elements at high concentration in the environments as they can persist in the soil for tens of thousands of years (Ghorbani et al, 2002). Soil ecosystems throughout the world have been contaminated with heavy metals by various human activities and movement of metals up the food chain has become a human health hazard (Naidu et al,1996). Heavy metal pollution of soil has been recognized as a major factor impending soil microbial processes (Stuczynski et al, 2002). Micro-organisms are far more sensitive to heavy metal stress than soil animals or plant

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growing on the same soils (Ghorbani et al,2002). Pollutants affect soil organisms in many different ways. Some organisms cannot survive, some are not affected. Consequences of this are changes in soil biota and biodiversity. Changes in biodiversity may cause changes in the basic ecological soil functions. Soil characteristics may be negatively influenced by the pollution. The usage of soil biological tests to characterize the composition and the functioning of soil biota are important (Domsch, 1991). Microbial parameters appear very useful in monitoring soil pollution by heavy metals. Microbial parameters that can be used as indicators in monitoring soil pollution by heavy metals fall into two main groups, the first is those that measure the activity of the whole microbial population. The second type measure the size of the microbial population (Ghorbani et al,2002). Some of these parameters are substrate- induced respiration (SIR), VAM spore numbers, total plate numbers of bacteria and actinomycetes. For this perspective, in both sides of Razan-Hamadan highway soil sampling was carried out and analyzed for total Pb, SIR, VAM spore numbers, bacteria and actinomycetes population. The effect of Pb pollution on microbial parameters was determined. Materials and Methods In both sides of Razan-Hamadan highway soil samples was taken on a transect (200-m long), in perpendicular direction from highway with a separation distance of 10-m, therefore, providing 20 actual sampling locations for each side of highway. Soil samples were taken from 0-15 cm depth with 3 replicates. All wet soils were passed through a 2 mm sieve, then stored in cold room at 4C ˸ for microbial studies. Soil samples were dried at 105 ˸C for 24 hrs for total Pb analysis. Total Pb of soil samples was extracted by 5N HNO3 and analyzed by atomic absorption on a PerkinElmer instrument at 283.3 nm using an air-acetylene flame (Ramos et al, 1994). Spores of VAM fungi were isolated from 50 cm3 sub-samples by wet sieving (Gerdemann and Nicolson 1963) and centrifugation (Jenkins, 1964), and counted (Sylvia, 1994). Substrate-induced respiration (SIR) (Anderson and Domsch, 1978), was determined in 72 h (Alef and Nannipieri, 1995). Bacterial and actinomycetes populations were estimated by plate count method. Soil suspension and dilutions were prepared. A solid medium was used for determination of total bacterial numbers. The media was prepared with Alef ´s method,1995. The medium used for determination of total actinomycete numbers was rose bengal, starch, casein, nitrate agar (RBSCN-agar) in various modifications including the adding of anti- fungal substances such as nystatin (mycostatin) at a concentration of 50 µg ml-1 (Davies and Williams 1970) or bacteriostatic dye rose bengal at 0.035 g L-1 (Ottow1972). All the statistic analyses were performed on the SAS 6.12 software. Results As shown in figures 1 and 2, a very high lead content in both roadside soils can be observed, however, total Pb contents of samples decreased with distance from the roadsides. The highest mean lead concentration of roadside may exceed 180µgg-1, while the background concentration is less than 20µgg-1. The highest mean lead concentrations were found in soil samples taken 10 meters form both roadsides. They were 84.83 ±2.23 µgg-1 in soil samples taken 10 meters from the west side of the road, and 180 ±16.36µgg-1 in soil samples taken 10 meters from the east side of the road. The lowest concentrations were measured in 200 meters form west (49.5±2.23µgg-1) and east (57.17±16.36µgg-1) of the road. Total Pb concentration was high on east of the road rather than west of the road. There were high significant differences between sample sites for total Pb concentration (P0, in cm-1) is related to the inverse of the air entry suction, and n (>1) is a measure of the pore-size distribution (Van Genuchten, 1980).

To render the PTFs of Rosetta as widely applicable as possible, a large number of records of soil hydraulic data and corresponding predictive soil properties were obtained from three databases (Schaap and Leij, 1998 and Schaap et al., 2001). Most of the samples were derived from soils in temperate to subtropical climates of North America and Europe. The textural classes of the datasets used for water retention are: sand, loamy sand, sandy loam, loam, sandy clay loam, silty loam, clay loam and silty clay loam (Schaap et al., 2001). Data from clays, silty clays, sandy clays and silts are rare. 3.2. SOILPAR 2

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SOILPAR 2 is a program for estimating soil parameters (Stockle et.al., 2003). It allows computing estimates of soil hydrological parameters using 15 procedures (see section 2.2) and comparing the estimates with measured data using both statistical indices and graphics. Twelve methods estimate one or more of the following characteristics: soil water content at predefined soil matrix tension, saturated hydraulic conductivity and bulk density. Three methods estimate the parameters of well-known soil water retention functions: Brooks-Corey, Hutson-Cass and van Genuchten, and one estimates both saturated soil hydraulic conductivity and the Campbell parameters of the soil water retention curve (Acutis and Donatelli, 2003). 4. Evaluation methods Usually, a common method to evaluate models is to plot the measured values against the predicted values and the correlation between them is used for model evaluation (Kobayashi and Salam, 2000). Ideally, this relationship should be linear with a slope of unity and intercept of zero. Although this method may be satisfactory for fitting an empirical model to observed data, it is inadequate for evaluating the performance of mechanistic models (Kobayashi and Salam, 2000). Generally, correlation-based statistics in conjunction with two other statistics, root mean squared error (RMSE), and mean deviation (MD), also called bias, are used to evaluate the performance of models. However, these statistics are not consistent with each other in their assumptions, therefore, Kobayashi and Salam (2000) have derived the following relationship among mean squared deviation (MSD), squared bias (SB), mean squared variation (MSV), squared difference between standard deviations (SDSD), and the lack of positive correlation weighted by the standard deviations (LCS) as follows: MSD = SB + MSV = SB + SDSD + LCS (9) n

where MSD = 1 n ∑ ( xi − yi )2

(10)

i =1

and xi is the simulated value, yi is the measured value, and n is the number of observations. SB = ( x − y )2 Where x and y are the average values of measured and predicted data.

(11)

n

MSV = 1 n ∑ [( xi − x ) − ( yi − y )]2

(12)

SDSD = (SDs − SDm )2

(13)

i =1

n   Standard deviation of simulated values, SDs = 1 n ∑ ( xi − x )2    i =1

12

n   Standard deviation of measured values, SDm = 1 n ∑ ( yi − y )2   i =1  LCS = 2SDs SDm (1 − r ) where r is the correlation coefficient.

12

(14)

(15) (16)

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The accuracy of model performance is usually judged from the correlation coefficient, r, however, MSD is a more comprehensive evaluator of model performance. It includes LCS, which incorporates the role of r in the computation of MSD. Moreover, the values of SB and SDSD can also provide greater insight into performance of a model.

Results and Discussion Mean squared deviation (MSD) and its components associated with the different methods of estimating field capacity and wilting point are given in Tables 2 and 3, respectively. The best and the worst observed and predicted values of field capacity and wilting point, on the basis of MSD computations, are given in Figures 1 and 2, respectively. The data points of the PTFs with the least MSDs are closer to the 1:1 line, whereas those with highest MSD are not. Instead of giving figures for all 13 PDFs, the regression parameters for field capacity are given in Table 2, and those for wilting point are given in Table 3. Ideally the intercept should be close to 0, however, for field capacity the intercepts were always higher than 0.13, and all significantly greater than 0 (P < 0.05) (Table 2). Similarly, the slope should be close to 1, however, in all the cases the slopes were less than 0.4, and significantly less than 1 (P 2, 2-1, 1-0.5, 0.5-0.25, and 2, 2-1, 1-0.5 and 0.5-0.25 mm in diameter were 1.5, 2.5, 7 and 14% respectively. Tisdall and Oades (1982) concluded that the stability of macro-aggregates (>0.25mm) is controlled by soil management (e. g., tillage, rotation, etc.), but the stability of micro-aggregates ( 6m/s, wind erosion faces, days of sandy winds with identical in year) Following the factorial scaling technique, to each of indicator is assigned a score ranging from 1 (good conditions) to 2 (deteriorated condition). Value "zero" is assigned to the areas where the measure is not appropriated and/or those which are not classified. The classes and the scores are based on the influence that various parameters have on the land degradation processes. In the most cases, the function representing the variation of the indicators (score) is liner ranging between the extreme values [1-2], although, in some particular cases, a nonlinear variation is possible. When the scores are assigned, the indicators are grouped and combined 3 quality layers representing Climate Quality Index (CQI), Water Resources Quality Index (WQI), Erosion Quality Index (EQI). The quality layers, obtained in such a way, do not depend on the structure of the input layers (number of classes, etc) and they are compared among them as equivalent ignoring the format of data/indicators. The values of Quality Index for each elementary unit within a layer are obtained as geometric average of scores of single indicators according to the following formula: Quality _ xij = (layer_ 1ij). (layer_2ij).....(layer_nij)1/n (1) Where i,j represent the “coordinates” (row and columns) of a single elementary unit of a layer and n is the number of layer use for determination of each quality layer. Defined by means of Thornthwait index I=[(P-ETP)/ETP]*100 where P=yearly average rainfall(mm); ETP=yearly average potential evapotranspiration (mm) n

1

2 The Bagnouls Gaussen aridity index (BGI) is defined BGI= ™(2ti-pi).k i=1 where ti is the mean air temperature for month i (ºc), pi is the total precipitation for month i (mm), ki represents the proportion of month during which 2ti-pi >0

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This conceptual framework allows for the integration of for quality layers over the whole area under counteraction as: ESAI = (CQI*WQI*CQI)1/3 (2) Where ESAI describes a synthetic Environmental Sensitive Index. When mapping the three quality indices, values are subdivided into three classes of equal rang (high: 1-1.33; medium: 1.34-1.66; low: 1.67-2).Similarly, risk of desertification in the final ESA map was classified as "high" (ESA from 1.75 to 2.0, "medium" (ESA from 1.5 to 1.74), "low" (ESA from 1.25 to 1.49) or "absent" (ESA from 1.00 to 1.240). All data, with a 1:50000 scale resolution, where integrated and processed in a GIS based on arc/info8, arc View3.2. RESULTS The result showed that 1.7% of the region hasn't been classified; these areas include urban, water body and etc that have no affect on desertification. 3% of the region fit into absent class no sign of desertification and shows proper area for agriculture and it is located at the southern part of the region, 1% of the region was allocated in low class, it means that land degradation has some little effect on it, and this area should be considered as a sensitive are. 59.2% of the region was allocated in the medium class, and desertification has obvious effects. Finally, 35.1% of the region is allocated in high class; these parts of plain are critical areas of desertification, which are located at the north, north western part of the region. DISSCUSSION The result of this study provide, desertification condition map, that declare desert land area, kind of process, dominant indicator, sub indicator and intensity class of desertification. Also, result showed high intensity of desertification and it's retrogression trend. According to studied processes, the deterioration of water resources is a great problem in these areas. Among affecting factors, wind erosion is dominant in destabilized and stabilized sand dunes in comparison to another areas. Phreatic decline and water quality change are most critical water factors, causing desertification. On the other hand, anthropogenic effects have played a main role in accelerated wind erosion and land degradation. In the past 3-4 decades, human activity such as clearance of shrub, clear cutting of forest and overgrazing have been the main factors triggering desertification. but in recent years the main factors that cause desertification in dry land ecosystems with water deficient are: growing population, industrial development, conversion of land to farmlands and then leaving them, intensive use to water resources that this study confirmed this exactly, in addition to absent of dominated crops (wheat, barley) indicated overexploitation of local water resources; despite of improvement and transient economic growth, it shows critical future. The rate of wind erosion has decreased significantly because of afforestation. So that local experts have estimated that intensity of wind erosion in the region is medium. Although intensity of wind erosion was very high before the afforestation in any case exact distribution of intense wind erosion classes have mentioned in the investigation. The model has recently drawn special attention among Iranian researchers and is more advantageous than other methods because of its accuracy, particular weighting of layers, use of geographical information systems in overlaying of maps, use of geometric mean than arithmetic one or sum in computing indices and final desertification map. In addition to a higher precision and speed of evaluating and preparing desertification map, there is a very little error using ESAs.

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REFRENCES 1_ Basso F, Belloti A, Faretta S, Ferara A, Manino G, Pisante M, Quaranta G, Tabemer M (1999) The Agri Basin In: MEDALUS Project_ Mediterranean Desertification and Land Use. Manual on Key indicators of desertification and mapping Environmentally Sensitive areas to desertification. 2_ Kosmas C, Gerontidis St, Detsis V, Zafiriou Th, Marathianou M(1999) Application of the MEDALUS methodology for defining ESAs in the Lesvos islsnd, European Commission. 3_ FAO/UNEP, Land Degradation Assessment in Dryland (LAND),(2001) United Nations Environment Program, Global Environment Facility (GEF). 4_ Ladisa G, Todorovic M, Trisorio_liuzzi G (2002) Characterization of Area Sensitive to Desertification in Southern Italy, Proc.Of the 2nd Int.Conf. On New Trend in Water and Environmental Engineering for Safety and Life: Eco-compatible solutions for Aquatic Environmental, Capri, Italy. 5_European Commission (1999) Mediterranean Desertification and LandUse(MEDALUS). MEDALUS Office. Landen. 6_ Kosmas C, Kirkby M, Geeson N (1999) European Commission The MEDALUS Project Mediterranean Desertification and Land Use. 7_ Rafiei EmM A(2003) Desertification vulnerability in Varamin plain.proc. Of the 6th Int. Conf. on Map India 2003, New Dehli, India.

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Figure 1. Characterization of the areas sensitive to desertification in Kashan

2% 3% 1%

no class

35%

absent low medium 59%

Figure 2. Repartition of the sensitivity in Kashan

high

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Effect of deforestation on selected soil quality attributes in loess-derived landforms of Golestan province, northern Iran Farshad Kiani1, Ahmad Jalalian2, Abbas Pashaee3 and Hossein Khademi4 1. College of Agriculture, Isfahan University of Technology, Isfahan, Iran. [email protected] 2. College of Agriculture, Isfahan University of Technology, Isfahan, Iran.E-mail:[email protected] 3. College of Agriculture, Gorgan University of Agriculture And Natural Resources Sciences, Gorgan. Iran.E-mail:[email protected] 4.: College of Agriculture, Isfahan University of Technology, Isfahan, Iran.E-mail: [email protected]

Abstract Deforestation and land use change have created serious problems in northern Iran. During the past three decades, forest coverage has decreased from 18 to 12.2 million hectares. To investigate the degree of forest degradation and the effect of land use change on some soil quality attributes in loess land, samples were selected from different land uses including forest, rangeland, degradated rangeland and farmland in Pasang watershed located in the Galikesh area, Province of Golestan, north of Iran (37°16'N, 55°30'E) with annual average temperature of 15 °C and a mean annual precipitation around 730 mm .pH, EC, amount of organic matter, CaCO3 and nutrients (N, P, K) as chemical indicators, hydraulic conductivity, bulk density and porosity as physical indicators and soil respiration as biological indicator were considered. Mean values of different variables were statistically compared by Duncan's method (p”0.05). The results have shown that the amount of organic matter decreased three units when it turns from forest to farmland, and increased two units from farmland to rangeland. The amount of CaCO3 in surface layer of deforested area was more than the forest soils. The amount of soil N in forest and soil P and K in rangeland were high. Root decomposition and uptake by plants had an important role on distribution of N. Difference in soil P storage may have resulted from changes in biological and geochemical processes. Weathering and leaching have affected soil K. Bulk density and porosity in forest and MWD in rangeland were higher than in other land uses because of decreasing the amount of organic matter and farming activity. Soil respiration in forest was highest as compared to other land uses. Decomposition of organic matter in farmland with no addition of plant residues have caused low respiration rate. So, we can suppose that amount of organic matter, soil N, Bulk density, porosity, MWD and soil respiration are suitable indicators for soil quality evaluation in this area. Understanding and determination of land use change and its effect on soil quality is a priority for researchers and policy makers. Keywords: Deforestation, land use change, soil quality, loess

1. Introduction Floodings in Golestan province are not unexpected events now. Every year this destructive event causes a lot of damage in farmlands and civil constructions. Deforestation and land use change have created serious problems in north of Iran. During the past three decades, the forest coverage has decreased from 18 to 12.2 million hectares. Although deforestation and land use\cover change has often been mentioned as the prime reason for flooding, but tree cutting is not taking the main responsibility. Forest degradation has also negative consequence on soil quality and health. Soil quality, defined by Islam(2000) is the capacity of a soil to function within the ecosystem boundaries and to interact positively with surrounding ecosystems.

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Various approaches for soil quality indexing have focused on two important principles associated with soil quality and its assessment. Firstly, soil quality indices are determined by using both inherent and dynamic properties and processes interacting within a living medium. Secondly, the indices are determined holistic consideration of biological, chemical, and physical properties, processes, and interactions within soils (Karlen 2003) .The loess derived soils of northern Iran are unique in terms of their characteristics and need special attention. Loess can be defined simply as terrestrial clastic sediment, composed predominantly of silt-size particles, which is formed essentially by the accumulation of wind-blown dust (Kemp 2001). Loess lands, as a useful landscape for agriculture (Catt 2001), are very susceptible to soil erosion. Thus, for land use planning and management in these lands more information about characteristics, properties and reactions are needed. The objectives of this study are to evaluate the soil quality indicators in forest, rangeland and farmland and to determine the effect of deforestation on these indicators. Materials and methods 2.1. Site description The study has been carried out in the Pasang catchment, located in the Galikesh area, Province of Golestan, North of Iran (37°16'N, 55°30'E).(Fig.1). This site is a part of the Goroanrood watershed with annual average temperature of 15 °C and a mean annual precipitation around 730 mm. The parent materials are composed of loesses with different ages, and the deep and moderately developed soils of the study area are classified as Alfisols according to Soil Taxonomy. The selected area is about 286 hectares. The whole 286 hectares of study area are covered by farmland, rangeland, degraded rangeland and natural forest with about 50, 15, 15 and 20 percent respectively. The main plant species are Carpinus.sp and Quercus.sp, in forest, Triticum.sp in farmland and Rosaceae.sp in rangeland. The studied rangelands are in fact the former farmlands that were conserved about 30 years ago. Rangeland has been partly grazed recently. 2.2. Soil collection and analysis For each land use, about 20 samples were randomly taken from surface soil (0-10cm), air dried and their chemical properties were measured. Six sites were selected for hydraulic conductivity and infiltration measurements. These measurements were done with six replication in each site. Ten fresh samples were used for soil respiration analysis. Mean values of different variables were statistically compared by Duncan's method (p”0.05). Results and discussion 3.1. Effects of land use on chemical indicators and soil nutrients The ANOVA results are presented in Table1.The pH value of the forest and deforested soils vary significantly .Basic ions were leached mainly in forest soils, consequently the acidity of forest area was more than other sites. Cultivation has redistributed the basic leached ions of lower horizons in surface layers of deforested section.The amount of forest soil organic matter, due to addition of tree residues and weak decomposition, was more than the other sites. Although human activity and farming have amplified the decomposition rate in rangeland, but the amount of organic matter compared to farmlands, was increased due to its land cover .Grazing

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has reduced the amount of organic matter in rangeland but it was not significant when compared with other sites. The amount of CaCO3 in surface layer of deforested area was more than the forest soils, which was supposed to be due to cultivation. Tillage seems to be responsible for uplifting of lime from underneath calcic horizon. Differences of CEC in all sites were not significant. Although, the difference of organic matter in soils of forest and farmland was high (about 4% against 1.3%), but due to slow rate decomposition of plant residues in forest soils and rapid mineralization of plant residues in farmland, it has not affected CEC in forest soils. Although plants play an important role in regulating the biogeochemistry of ecosystems by fixing the nutrients under disturbance (Xiongwen Chen 2003), but human activities in the forest soils has had great a impact on soil nutrient storage. Soil nutrient had decreased because of (i) residue burning, plowing, and loss of nutrients (ii) tree cutting, moisture loss after tree cutting (iii) species changing. Plant species have different nutrient requirements, and exploit nutrients with varying efficiently, so regulate the soil nutrients with different rates. Different land uses had different effect on soil nutrient circulation. Soil Nitrogen had a high correlation with the amount of organic matter in different sites. and it was completely shown that root decomposition and uptake by plants had an important role on distribution of N. Difference in soil P storage may be resulted from changing of biological and geochemical processes at four land types. Biological controls on P include root growth pattern, amount and quality of detritus inputs , exteracellular enzyme activity, production of organic chelates and micorrizal activity (Xiongwen Chen 2003).In study area , soil P in forest were higher than farmland but lower than rangelands and these differences were not significant. It is supposed that, rangeland plant coverage was more efficient to releasing the P from phosphorous minerals, P up taking and storing in their tissues. However it would be related to other factors other than land use. The amount of soil K was the most in rangeland then in forest, degraded rangeland and farmland respectively. Potassium is reduced by tree cutting because of increasing the leaching processes in degraded forest. High amount of K in rangeland may be resulted from loess weathering and storage by rangeland plants and in farmland, K may be reduced due to leaching.

3.2. Effects of land use on physical indicators Soil texture in study area varied from silty clay to silty clay loam. Soils under cultivation had higher bulk density than forest and rangeland (Table 2) because of having higher amount of organic matter with low weight effect on bulk density. Cultivation has positive affect on soil porosity. So, in forest soils the porosity was less than the other land uses. Organic matter in forest and rangeland soils seems to bridge the loess particles. This action causes to encrustation of organic matter in forest soil surface. Cultivation and plowing in farmland increase the soil infiltration. Because of stable soil structure in forest, it has significantly high hydraulic conductivity rate than the other sites. Difference between value of MWD in farmland and other land use types was significant because of decreasing the amount of organic matter and farming activity. 3.2. Effects of land use on soil microbial respiration In soil quality assessment, the biological indicators play a great role. Biological indicators are most sensitive to change and show the differences better. Enzyme activities, soil respiration and microbial biomass analysis help us to understand health

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condition of soils. In this paper soil respiration as a biological soil quality indicator was measured. The difference between lands uses was statistically significant (Table.3).High amount of respiration in forest may be due to high new organic matter (litter) that is annually added to soil surface. Decomposition of organic matter in farmland without adding the new ones seems to have caused low respiration rate.

Conclusion Our study showed that deforestation in north of Iran has influenced the soil quality indicators. In many indicators tree cutting decreased the soil quality and resting the farmland and changing to rangeland improved its quality and grazing of this rangeland negatively effect on this improvement. In chemical soil quality indicators, pH and CaCO3 in forest were low and in contrast organic matter and N were high compared to other land uses. Soil K and P were high in rangeland and CEC did not change significantly. In physical soil quality indicators, bulk density and porosity were in lower and Ksat was in higher level in forest. MWD was increased in rangeland and was decreased by farming. Soil respiration as a biological indicator of soil quality was increased by organic matter and was in highest level in forest. So, we can suppose that amount of organic matter, soil N, Bulk density, porosity, MWD and soil respiration are suitable indicators to evaluation of soil quality in this area. Understanding and determination of land use change and its effect on soil quality is a priority for researchers and policy makers. Acknowledgements We would like to thank the staff laboratory, Soil Science Department, College of Agriculture, Isfahan University of technology. References Catt J ( 2001) The Agricultural importance of loess. Eearth Science Reviews 54: 213-224. Islam KR , Weil R (2000) Land use effects on soil quality in a tropical forest of Bangladesh. Agriculture Ecosystems &Environment 79:9-16. Karlen DL (2003) Soil quality: why and how. Geoderma 114: 146-156. Kemp RA(2001) Pedogenic modification of loess: significance for paleoclimatic reconstructions . Earth Science Reviews 54:145-156. Xiongwen Chen , Bai-Lian Li(2003) Change in soil carbon and nutrient storage after human disturbance of primary Korean pine forest in Northern China. Forest Ecology and Management 186:197-206.

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Fig.1. Location map of the study area Table 1 .Effect of land use on selected chemical soil quality indicators in Pasang watershed. A, B and C show Duncan grouping at p”0.05 Soil Properties

Forest

Rangeland

pH Organic matter (%) CaCO3 (%) N (%) P (ppm) K (ppm) CEC (meq/100gr)

7.09B 4.038A 24.85C 0.23A 70.96A 254.4B 13.21A

7.32A 3.14 B 28.98B 0.18B 77.32A 394.0A 13.16A

Degradated rangeland 7.41A 2.77B 31.50A 0.16B 69.08A 228.5B 13.00A

Farmland 7.49A 1.36C 28.7B 0.07C 69.0A 214.0B 12.97A

Table 2 .Effect of land use on selected physical soil quality indicators in pasang watershed. A, B and C show Duncan grouping at p”0.05 Soil Properties

Forest

Bulk density (gr/cm3) Porosity (%) Ksat (cm/hr) MWD (mm)

1.28A 34.79B 32.43A 1.88A

Rangeland

Degradated rangeland

1.23A 42.59 AB 8.32B 2.14A

1.24A 42.20AB 8.66 B 2.04A

Table 3. Effect of land use on soil respiration (mg co2/gr soil.day) in Pasang watershed. A, B and C show Duncan grouping at p”0.05 Forest 0.61A

Rangeland

Degradated rangeland

0.60AB

0.59AB

Farmland 0.51B

Farmland 1.31A 44.46A 1.46B 0.67B

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MICROBIAL ECOLOGY OF NITROGEN CYCLE IN SOILS AND ECOSYSTEM FUNCTIONING Alexander V. Kurakov Moscow State University, International Biotechnology Center and Soil Biology Dept., Moscow, Leninskie gory, 119992, Russia, Phone: 7-095-9393546, fax: 7-095-9395022, E-mail: [email protected]

Abstract The quantitative assessment of the roles of fungi and bacteria in nitrogen (N) transformation in virgin and cultivated soils was done. Measurements of biomass of bacteria and fungi, selective inhibition of their activities in the soils and comparisons of activities of inoculated fungal and bacterial strains in sterile soils with rates of N processes in native soils were performed in laboratory experiments. Fungi were responsible for most of ammonification (80%) of ready available N compounds (peptone) added to the forest soddy-podzolic soil. In the arable soil bacteria and fungi had an equal contribution to the mineralization of peptone to NH4+. Heterotrophic nitrification (mainly fungal) was higher in virgin soils than in cultivated soils of the same types. Contribution of heterotroph microorgamisms in nitrate production was highest in the soil under mature spruce (about 90%); it was much less (10-40%) in the soils of other natural ecosystem (leaf forests, grasslands). Autotrophic bacteria were responsible for 87-97% of nitrate formation in cultivated soils. N2O production by fungi attributed less than few percents to denitrification potential of soils. Cycloheximide depressed N2-fixing activity of soils enriched by plant residues on 60-70%. It proves that fungal extracellular hydrolysis of plant polymers provided major part of available carbon for heterotroph N2-fixing bacteria in soils. Fungi play a leading role in microbial immobilization of N in soils. Their biomass predominates (60-90% of total microbial biomass) in the majority of soils, particularly in the soils of the natural ecosystems. It was accounted that synthesis by fungi such resistant to degradation compounds as melanin and melanin-chitin complexes is one of key mechanism of maintenance and accumulation soil organic N. Functional capacities of bacteria and fungi in N processes in many aspects have duplicative or additive character. It could be considered as a factor of resiliency of N cycle in terrestrial ecosystems. Fungi cause maintenance and long-term deposition of N in soils. Increase of bacterial dominance in soils intensifies N turnover and due to their major role in nitrification and denitrification losses of soil N elevates. Destruction of native vegetation, conventional farming lead to a shift from fungal to bacterial dominance in soil N processes that increase N losses. Alternative agricultural systems that maintain a high level of fungal biomass could be more effective than conventional systems in retaining the N capital of the soils. Key words: bacteria, fungi, ecosystem, nitrogen, soil

Introduction The solution of many global ecological problems and elaboration of perspective agricultural systems impossible without extension our knowledge about nitrogen (N) cycle in soils. Microbial ecology of N turnover has largely focussed on activity and population’s density of N2-fixing, ammonifying, nitrifying and denitrifying bacteria. Diverse other soil organisms also utilise and transform N compounds. Fungi are most important between them due to high biochemical activity and biomass. However, their functioning in soils as a rule does not taken into account. That situation has built up because insufficient efforts were made to exploit metabolic capacity of fungi in N transformation, undervalue of predominance of fungal biomass in many soils and

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interrelation of N and C cycle, in which the basic role of fungi, as main reducers of organic residues, was accepted. It can be suggested that there is a connection between inputs of bacteria and fungi in N processes and rate of N turnover in soils and scales of N losses, correspondingly. The quantitative assessment of roles of fungi and bacteria in N transformation in virgin and cultivated soils is necessary for elaboration this question. Materials and Methods The samples of humus horizons of different types of soils (soddy-podzolic soil, gray forest soil, leached chernozem, brown carbonate soil and some others) from agricultural lands and natural ecosystems were used. Detailed study was done with soddy-podzolic soils collected on the territories of Soil-Ecological Station of Moscow State University (47 km to north from Moscow) and Central Forest State Reserve in Tver’s region. Soil samples (10 cores per the site) were taken from humus horizon beneath mature fir-grove-wood sorrel and from the field under cereallegume rotation with average dozes of NPK – 120 kg.ha-1 (mineral and organic fertilizers) and the field under monoculture of potato with organic fertilizers (5-15 t ha-1). Chemical properties of the forest and the field soils were: pH 3.9-4.3 and 5.9-6.4, total N – 0.09-0.27% and 0.080.14%, cation exchange capacity – 3-8 and 7-28 meq.100 g-1, respectively. Determinations of the soil chemical characteristics were done in air-dried subsamples by standard methods. Microbiological analyses were performed in fresh mixed samples after their sieving through 2mm sieve. Count of bacterial cells and fungal mycelium and spores were made by fluorescent microscopy with staining of calcofluor white and acridine orange for fungi and bacteria, respectively. Fluorescein diacetate was used for estimation of alive mycelium. The biomass of dark-colored and light-colored mycelium was assessed by light microscopy according Hanssen membrane filter method (Zvyagintsev, 1991). Dry weight of one bacterial cell (V=0.1-0.25 µm3) was accepted as 2.10-14-5.10-14 g, dry weight of 1 m of fungal mycelium (dia 5 µm) – 3.9.10-6 g and fungal spores (dia 5 µm) – 1.10-11 g (Zvyagintsev, 1991, Kurakov, 2003). The following values of N content in microorganisms were used for the assessment of N immobilization: bacteria – 4% (Ausmus et al., 1976), fungal spores – 8.7% (Foster, 1950), fungal alive mycelium – 3,0% and dead mycelium – 1,7%. The data of N content in mycelium were obtained in our preliminary investigation for fungi growing in the soddy-podzolic soils on decaying straw. Microbial biomass was assessed also by substrate induced respiration (SIR) method with antibiotics for selective inhibition of bacterial and fungal activity and fumigation-extraction method (West, Sparling, 1986, Zvyagintsev, 1991). The data of biomass C obtained by SIR method were converted to biomass N with ratio C/N=5 for bacteria and C/N=10 for fungi. Activity of N transformation was determined by adding corresponding N-containing compounds in the soil samples (5,0 g) placed in the glass bottles (15-25 cm3). The end product of investigated process was measured after necessary period of incubation of the samples at optimum temperature and moisture. Moisture of the soils was 60% of WHC (18% of H2O) in all experiments, except the study of denitrification where it was 50% of H2O content in the soils. All experiments were done in triplicates of samples and data were statistically treated. The rates of nitrification ammonium and peptone and reduction of nitrate and nitrite were assessed for pure cultures of fungi growing in sterile soils. Strains that demonstrated corresponding activities in the media were chosen for the investigation (Kurakov, 2003).

Nitrogenase activity of sterile soil enriched by starch and plant residues (1%) was investigated under inoculation of N2-fixing strain of Bacillus mycoides Л1 together with and without hydrolytic active strain of Trichoderma asperellum МГ6.

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Activity of N mineralization was estimated by incubation of the soils during one month at 370C without and with adding of humic acid. The humic acid of bog soil that had 2.8% N was applied at the rate 6,25 mg.g-1 soil. Maximum rate of inorganic N accumulation was detected after the first week and these values were used for comparison of the soils. Activity of peptone ammonification was assessed by measuring of ammonium in the samples enriched by peptone (5 mg.g-1) after incubation for 5, 12, 18 and 24 hours at 250C. Arginine ammonification was determined by short-term incubation (3 hours) of soil samples (Alef, Kleiner, 1986). Streptomycin sulfate (3 mg.g-1) and cycloheximide (4-16 mg.g-1) were used for the assessment of the participation of fungi and bacteria in ammonification of peptone, nitrification, denitrification and CO2-emission. The influence of fungi on N2-fixation in the soddy-podzolic soils enriched by starch and different plant residues (mixed meadow grasses, leaves of birch, willow, spruce, hazel) at the rate 1% was studied also by inhibitory approach The comparative assessment of autotrophic and heterotrophic nitrification in the soils was carries according to the follow plan with application of inhibitor nitrifications (IN): 1) soil, 2) soil + (NH4)2SO4, 3) soil + peptone, 4) soil + IN, 5) soil + (NH4)2SO4 + IN, 6) soil + peptone + IN. (NH4)2SO4 and peptone were added at the rate 100 µg N.g-1 of air-dried soil. Nitrapyrin - 2chloro-6-(trichloromethil)pyridine (20 µg.g-1) and aminotriazole (ATC) – 4-amino,1,2,4-triazole (100 µg.g-1) were used as IN. The IN at the rate 50% of initial dozes was added to the soils additionally after 7-10 days of incubation. Nitrapyrin and ATC specifically inhibite autotrophic nitrifying bacteria and methanotropic bacteria and do not influence on heterotrophic nitrifying microorganisms at same dozes (Bedard, Knowles, 1989; Kurakov, 2003). Soils were incubated at 26-280C. Nitrate concentration in the soils was measured at 3-5, 7-10, 14, 21-25 days and maximum activity of the process and level of nitrate production were used for comparative analysis of the soils. Populational density of autotrophic nitrifiers in the soils was estimated by most probably number method (Page et al., 1982). Activity of denitrification in the soils was determined by acytelene method (Zvyagintsev, 1991). For measurement of the potential activity in soil placed in glass bottles 1 ml of H2O solution of glucose (1,25%), 1 ml of KNO3 (0,4%) and 4 ml of H2O were added. The bottles were tightly closed by rubber stoppers and were flushed by argon for 1 min. 1 ml of acetylene was injected in gas phase of the bottles and then they were incubated at 280C for 24 hours. Formation of N2O monitored by means of gas chromatography (Models 3700/4 and 3700, Moscow Chromatography Plant) equipped with a Polisorb column (30 m) and a molecular sieve 10 A column (3 m) with thermal conductivity and electron capture detectors, and helium as gas carrier. Contribution of microscopic fungi in denitrification was done by comparison of activity of N2O release by the strain Fusarium oxysporum 11dn1 (Kurakov et al., 2000) inoculated in the sterilized soils with denitrifying activity of the native soils. Soil samples were sterilized 3 times in autoclave (1 atm, 121oC). Bacterial contamination was checked before and after the experiment by plating of soil particles on glucose-peptone agar. No evidence of contamination was noted. Fungal mycelium was obtained on Czapek agar for 3-5 days and was inoculated in the soils at the rate 0,5-1 mg.g-1. N2O in gas phase of the bottles was monitored on 1, 2, 5, 7, 9, 12 days. Control treatment of sterile soils contained autoclaved mycelium. There were treatments with sodium nitrite (2 µg N.g-1), sodium nitrate (50 µg N.g-1) and with and without glucose (2,5 mg.g-1). Treatments with F. oxysporum 11dn1 had not acetylene in the gas phase, because acetylene does not influence on N2O formation by fungi (Kurakov et al., 2000).

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Results Fungal biomass predominated under bacterial biomass in the soddy-podzolic soils according both luminescent microscopy and SIR method and composes around 70-90% of total microbial biomass (table 1). Pool of the microbial biomass was several times higher in the forest soil than in the cultivated soil. This difference in biomass was connected with decrease of amount of fungal mycelium in the soil of agroecosystem. Biomass of fungal spores and bacteria were similar or higher in the cultivated soil. Consequently agricultural treatment of the soddy-podzolic soil leads to the significant decrease of ratio fungal biomass to bacterial biomass. Microbial immobilization of N in humus horizon of the forest soil reached 85-211 µg N.g-1 that was 5.6-17.1% of total soil N. It is around 2-3 times higher than microbial immobilization of N in the plough horizon of the field soil. Predominated amount of N was immobilized in fungal biomass compared to bacterial biomass that was reached up to 90% of microbial N immobilization in the forest soil and 55-75% in the arable soil. Signifficant aspect of N turnover in soils that escape often from consideration is long-term retain of N in melanins formed by microorganisms, mainly by dark-colored fungi. Fungal melanins have very close chemical properties with humic acids and stability of melanins to microbial degradation draw near such for humic acids (Zavgorodnyaya et. al., 2000). Portion of dark-colored fungi in the arable soddy-podzolic soil was 20% and in the forest soil 50-70% of total fungal biomass. Average content of melanins in the dark-colored fungi is around 2% and N content is 1-5%. Quantity of N that fixed in fungal melanins composes around 0,0020,07% of total soil N in these soils. Number of turnovers of alive mycelium is 5-15 in the soddypodzolic soil under agroecosystems and 2-5 - under fur forest during vegetation session (Kurakov, 2003). So, the annual contribution of fungal melanin's N to total soil N will be up to 0.04-0.1% in the forest (with taking into account of fungi of the litter up to 0,07-0,2%) and markedly less - up to 0.005-0.01% in the arable soil. The important withdrawal consists that leading role in immobilization and maintenance of stable organic forms of N in soils belongs to fungi, especially, under the natural forest compared to the agroecosystem. Activity of processes of N transformation sharply intensified after adding to the soils Ncontaining compounds (arginine, peptone, humic acid, sulfate ammonium, potasium nitrate) (table 2). What is necessary to emphasize that the increase of rate of all N processes was significantly higher in the cultivated soil compared to the forest soil. So activity of peptone and arginine ammonification was higher in the cultivated soil than in the forest soil, although total microbial biomass was considerable (4-5 times) lower in this soil. It was shown that fungi predominated in ammonification of peptone in the soil of forest ecosystem (input in the accumulation of ammonium was 70-90%). Fungi and bacteria had an equal contribution to the mineralization of peptone to NH4+ in the arable soil. The enlargement of ammonifying activity in the cultivated soil was connected with increase of bacterial biomass and specific activity both bacteria and fungi in the soil, that compensated significant decline biomass of fungal mycelium. Therefore fungi predominate in ammonification of ready available nitrogen compounds if their biomass significantly (around 10 times) exceeds bacterial biomass in soils. Although oxidation of N compounds by heterotrophic microorganisms does not connect with obtaining of energy and their activity considerably less than autotrophic bacteria demonstrate, heterotrophs, particular fungi, have shown to exhibit nitrification activity under conditions that are realistic for acid forest soils. Nitrifying capacity was demonstrated for fungi Aspergillus flavus, A. wentii, A. niger and some others - Absidia cylindrospora, Verticillium lecanii (De Boer, Kowalchuk, 2001, and others). Wide spectrum of species – Penicillium nigricans, P. janthinellum, P. chrysogenum, P. martensii, P. lanoso-coeruleum, P. melenii, P. canescens,

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Alternaria alternata, Gliocladium roseum, Fusarium solani isolated from soddy-podzolic soils was able to produce nitrate or nitrite under growth in media with ammonium or organic N (βalanin or peptone) (Kurakov, 2003). Nitrate production by some of these species (P. janthinellum, Penicillium nigricans, P. martensii, A. flavus, A. niger) was reached up to 2-16 µ g.g-1 for 10 days under their incubation in sterile soddy-podzolic soil. Nitrate production by fungi increased after addition of peptone and, contrary, ammonium, in majority cases, has not had such influence on nitrifying activity of fungi growing in the sterile soil. Rate of nitrification on application of sulfate ammonium had different respond in the cultivated soil and the soil from the fur-wood sorrel (table 2). Activity of nitrate formation has not changed in the forest soil enriched by sulfate ammonium and increased in the cultivated soil. Peptone leaded to enlargement of nitrification activity in the both soils. Quantitative assessment of heterotrophic nitrification in these soils was obtained by application of specific inhibitors of autotrophic nitrifying bacteria and methanotropic bacteria. Similar results were obtained with aminotriazole and nitrapyrin. It was shown that activity of heterotrophic nitrifying microorganisms was higher in the soil under the fur than cultivated soil (table 2).. Heterotrophic nitrification predominated in the forest soddy-podzolic soil (75-94%) and the gray forest soil (43-62%). Predominance of fungi in the heterotrophic nitrification in the soils was verified much more (5-10 times) strong suppression effect of cycloheximide than streptomycin sulfate on nitrate formation. The heterotrophic production of nitrate in the undisturbed steppe soils was 3-5 times less (15-33%). Autotrophic nitrifying bacteria were responsible for 87-97% of the nitrate formation in the all types of cultivated soils. Populational density of autotrophic ammonium and nitrite-oxidating bacteria was on many orders lower in the soils from natural ecosystems compared to the soils of agroecosystems. For example, in the fur wood sorrel their number was not higher than 10 cells.g-1 and in the field soil was 1100-11600 bacterial cells .g-1 of group Nitrosomonas and 400-4300 cells.g-1 – Nitrobacter. It confirms different nature of main nitrification agents in the cultivated and the undisturbed acid soddypodzolic soils. Although substantial evidence now supports the role of chemolithotrophic bacteria as the main nitrifying agents in most acid soils (De Boer, Kowalchuk, 2001), heterotrophs apparently makes considerable input in nitrification in the acid soils that is component of undisturbed climax coniferous forests. It was shown that rather many species of fungi, especially, belonging to genera Fusarium, Cylindrocarpon, Chaetomium, Trichoderma, Talaromyces, Neosartorya demonstrate activity of N2O production under anaerobic conditions on nitrite-containing media. Species of Fusarium, Cylindrocarpon were most active and strains of F. oxysporum were able to produce N2O on nitrite- and nitrate-containing media. Activity of N2O evolution by fungi compared to denitrifying bacteria was several orders lower that indicated a detoxication mechanism of N2O production by fungi (Kurakov et al., 2000). Application of inhibitory approach with antibiotics has shown that bacteria predominated (>90-95%, according to accuracy of the method) in anaerobic reduction of N to gaseous compounds in the soils. Fungal input in the emission of N2O determined by comparison of level of N2O release by active strain Fusarium oxysporum 11dn1 inoculated in the sterile soil with denitrifying activity of non-sterile soddy-podzolic soil composed from portions of percent to several percents (up to 8%) (table 3). N2-fixation was in 2-4 times higher in the sterile soddy-podzolic soil enriched by cellulose (filter paper) or plant residues after inoculation of N2-fixing bacteria Bacillus mycoides Л1

together with cellulolytic fungus Trichoderma asperellum МГ6 than B. mycoides Л1 without T. asperellum МГ6. Activity of N2-fixation of native soils supplemented with various

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plant remains (meadow grass and leave residues) and starch was significantly (on 60-90%) decreased under application of antifungal antibiotic cycloheximide in the soils. Discussion Participation of bacteria and fungi in N processes has in general additive character that provides stability of functioning of N cycle in soils. Thus, relatively few fungal species are able to reduce NO3-/NO2- to N2O in the soils effectively as a high diversity of denitrifying bacteria. Conversely nitrification is carried out mainly by chemolithotrophic nitrifying bacteria that has low diversity but many species of fungi can underpin this process in undisturbed acid soils where populations and activity of autotrophic nitrifiers are limited. The contribution of fungi and bacteria in ammonification and N immobilization are affected by their activity and biomass in various soil conditions associated with different ecosystems and their management. Fungi can not fix N2, but fungal extracellular hydrolysis of plant polymers is critical for supplying of free-living N2-fixing bacteria with readily available carbon and energy sources. Bacteria are almost entirely responsible for anaerobic reduction of N, predominate in nitrification and their contribution is significantly less in ammonification of readily available N-containing compounds and N immobilization in the soils. Fungi have the leading position in N immobilization in the soils, in less degree – in ammonification, and their role in oxidation of N compounds can be notable only in the undisturbed acid soils of natural ecosystems under climax fur forest mainly and much less in broad-leave forest and grassland. Destruction of natural vegetation and agricultural management of soils modifies relative role of fungi and bacteria in N processes. In general, the role of fungi in N cycle is more significant in native ecosystems. Importance of bacteria increases in soils of agroecosystems that leads to intensification of N turnover. Because bacteria have higher metabolic activity, particularly with regard to the oxidation-reduction of N it increases N losses from soils, but also provides for crops more fast supply of necessary quantity of N in short-term period. Consequently, it can be stated that conventional agricultural management, tillage, destruction of native plant vegetation lead to bacterization of soils and decrease of matter of fungi. Alternative agricultural systems that direct on internal stability due to maximum return of organic residues and composted substrates can be one of way to support of high biomass and activity not only bacteria, but also fungi, that can raise N retaining in soils. Increase of the ratio of fungal to bacterial biomass, decline of rates of ammonification and emission of N2 and N2O was observed under application of organic fertilizers to the soddy-podzolic soil compared to mineral fertilizers (Kurakov, 2003). References Alef K., Kleiner D. (1986) Arginine ammonification, a simple method to estimate microbial activity potentials in soils. Soil Biol. Biochem. 18: 233-235. Ausmus B.S., Edwards N.T. and Witkamp M. (1976) Microbial immobilization of carbon, nitrogen, phosphorus and potassium: implications for forest ecosystem processes. In Anderson J.M. and MacFadyen A. (eds.) The Role of Terrestrial and Aquatic Organisms in Decomposition Processes. p.397416, Blackwell, Oxford. Bedard C., Knowles R. (1989) Physiology, biochemistry, and specific inhibitors of CH4, NH4+, and CO oxidation by Methanotrophs and Nitrifiers. Microbial. Rev. 53: 68-84. De Boer W., G.A.Kowalchuk (2001) Nitrification in acid soils: micro-organisms and mechanisms. Soil Biol. Biochem. 2001: 853-866. Foster D. (1950) Chemical activity of fungi. Moscow, Forein Literature Press, 651 p. Kurakov A.V., Nosikov A.N., Skrynnikova E.V., L’vov N.P. (2000) Nitrate reductase and nitrous oxide production by Fusarium oxysporum 11dn1 under aerobic and anaerobic conditions. Current Microbiology: 41: 114-119.

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Kurakov A.V. Fungi in Nitrogen cycle in soils. D.Thesis (rus). Moscow. Moscow Univ.Max Press. 2003.50 p. Page A.L., R.H.Miller, and D.R.Keeney (ed.). (1982) Methods of soil analysis. Part 2. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI, USA. West A.W., G.P.Sparling. 1986. Modifications to the substrate-induced respiration method to permitmeasurement of microbial biomass in soils of differing water content. J. of Microb. Methods 5: 177-180. Zavgorodnyaya Yu.A., V.V.Demin, A.V.Kurakov (2000) Biochemical degradation of soil humic acids and fungal melanins. Proced. of 10th Intern. Meeting of the International Humic Substances Society. France, 2:1043-1047. Zvyagintsev D.G.(Ed.) (1991) Methods of soil microbiology and biochemistry. Moscow. Moscow Univ. Press. 250 p.

Table 1:N immobilization by fungi and bacteria in the soddy-podzolic soils Variant Biomass, mg.g-1 soil N pool in biomass, Portion of N of biomass in . -1 total soil N, % µgN g soil Fungi Bacteria Fungi Bacteria Fungi Bacteria Forest 2.1-5.6* 0.01-0.4 81-167 4-43 5.3-14.0 0.3-3.1 Field 0.6-1.1 0.09-0.6 39-48 8-55 3.1-4.3 0.5-4.5 *- range of variation of data (generalized) according to luminescent count, FE and SIR method

Table 3: Nitrous oxide production by Fusarium oxysporum 11dn1 in the sterile soil and denitrifying activity of native soddy-podzolic soil nmol N2O.(g.h)-1

Variant*

F. oxysporum sterile soil Native soil

in

without supplements

NO2-1

NO3-1

NO3-1 + glucose

trace-0,3**

2,5

0,5

1,0-4,1

6,1 -1

45,4 . -1

-1

6,5 . -1

75,2-214 -1

* - H2O content – 50%, NO2 – 2 µg N g , NO3 - 50 µg N g , NO3 (50 µgN.g-1) + glucose (2,5 mg.g-1), F. oxysporum in sterile soil - atmosphere Ar, non-sterile soil - Ar + C2H2, ** coefficient varition of data for the non-sterile soil – 15% and for F. oxysporum in the sterile soil – 50%.

Table 2:Activity of N transformation in the soddy-podzolic soils Process forest Mineralization, soil 0.34

µN .(g.h) –1 field 0.16

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Mineralization, soil + humic acid Ammonification, soil + arginine Nitrification, soil Nitrification, soil + nitrapyrin Nitrification, soil + (NH4)2SO4 Nitrification, soil + (NH4)2SO4 + nitrapyrin Nitrification, soil + peptone Nitrification, soil + peptone + nitrapyrin Denitrification, soil Denitrification, soil + glucose + KNO3 *- coefficient variation of data - 6-15%.

0.39 3.60 0.15 0.14 0.13 0.12 0.18 0.16 0.50 1.61

558 0.30 9.70 0.12 0.02 0.47 0.03 0.43 0.03 0.57 6.18

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